code: def _binary_recall_at_fixed_precision_compute(
      state: Union[Tensor, Tuple[Tensor, Tensor]],
      thresholds: Optional[Tensor],
      min_precision: float,
      pos_label: int = 1,
      reduce_fn: Callable = _recall_at_precision,
  ) -> Tuple[Tensor, Tensor]:
      (
          precision,
          recall,
          thresholds,
      ) = _binary_precision_recall_curve_compute(
          state, thresholds, pos_label
      )
      return reduce_fn(precision, recall, thresholds, min_precision)
  
file_path: torchmetrics/src/torchmetrics/functional/classification/recall_fixed_precision.py
imports: from typing import Callable, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.precision_recall_curve import _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_compute, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_compute, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_compute, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 4 for calling a global function with example usage: _binary_recall_at_fixed_precision_compute(state, thresholds, min_precision, pos_label, reduce_fn) and returns: reduce_fn